Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study

Objective To develop and validate a hybrid model incorporating CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics signatures for predicting progression of white matter hyperintensity (WMH). Methods A total of 226 patients who received coronary computer tomography angiography (CCTA) and brain magnetic resonance imaging from two hospitals were divided into a training set (n = 116), an internal validation set (n = 30), and an external validation set (n = 80). Patients who experienced progression of WMH were identified from subsequent MRI results. We calculated CT-FFR and pFAI from CCTA images using semi-automated software, and segmented the pericoronary adipose tissue (PCAT) and myocardial ROI. A total of 1,073 features were extracted from each ROI, and were then refined by Elastic Net Regression. Firstly, different machine learning algorithms (Logistic Regression [LR], Support Vector Machine [SVM], Random Forest [RF], k-nearest neighbor [KNN] and eXtreme Gradient Gradient Boosting Machine [XGBoost]) were used to evaluate the effectiveness of radiomics signatures for predicting WMH progression. Then, the optimal machine learning algorithm was used to compare the predictive performance of individual and hybrid models based on independent risk factors of WMH progression. Receiver operating characteristic (ROC) curve analysis, calibration and decision curve analysis were used to evaluate predictive performance and clinical value of the different models. Results CT-FFR, pFAI, and radiomics signatures were independent predictors of WMH progression. Based on the machine learning algorithms, the PCAT signatures led to slightly better predictions than the myocardial signatures and showed the highest AUC value in the XGBoost algorithm for predicting WMH progression (AUC: 0.731 [95% CI: 0.603–0.838] vs.0.711 [95% CI: 0.584–0.822]). In addition, pFAI provided better predictions than CT-FFR (AUC: 0.762 [95% CI: 0.651–0.863] vs. 0.682 [95% CI: 0.547–0.799]). A hybrid model that combined CT-FFR, pFAI, and two radiomics signatures provided the best predictions of WMH progression [AUC: 0.893 (95%CI: 0.815–0.956)]. Conclusion pFAI was more effective than CT-FFR, and PCAT signatures were more effective than myocardial signatures in predicting WMH progression. A hybrid model that combines pFAI, CT-FFR, and two radiomics signatures has potential use for identifying WMH progression.

According to the lesion prediction algorithm (LPA) in the LST (www.statistical-modelling.de/lst.html)toolbox of SPM12, WMH was automatically segmented using the lesion prediction algorithm for 2D images.The WMH volume was measured using a 1 mm 3 spatial dimension of a voxel in each MRI slice.In this process, further automatic segmentation and correction of WMH were carried out, including eliminating nonbrain matter and refining WMH segmentation.LPA does not require users to set special parameters, and the segmentation effect is faster and more sensitive than the lesion growth algorithm (LGA).In order to minimize the excessive segmentation of LPA, two experienced radiologists (8 years and 5 years of radiology neurologists respectively) independently observed the results.Images that were considered by both radiologists to have significant segmentation errors were manually segmented and measured using itk -snap software (http://www.itksnap.org/pmwiki/pmwiki.php)again.The final corrected image is used for WMH calculation.To display the temporal change of WMH, the volume of WMH on FLAIR images was measured at baseline and at follow-up.

Details of scan parameters and CCTA
All CCTA examinations were performed with a CT scanner using 64 detector rows with prospective electrocardiogram (ECG) -gating (ZJP Hospital: Somatom Flash, Siemens Healthineers, Forchheim, Germany; TCM Hospital: Aquilion One, Toshiba Medical, Otawara, Japan) with the same parameter settings.The tube voltage of the CCTA was a fixed value of 100kV, and the tube current was adjusted based on the patient's BMI, with a reference range of 350-500 mA.During scanning, the scavenging range was set to 1 cm below the tracheal bulge to the level of the cardiac diaphragm.
The frame rate is set to 0.35 s/circle, the reconstruction layer thickness is 0.5 mm, and the reconstruction layer interval is 0.25 mm.The image acquisition of CCTA was performed by ECG gated scanning technology, HR < 65 hpm, single sector recombination was implemented, the time of exposure was 70%-80% R-R interval, one cardiac cycle was collected; HR ≥ 65 bpm, multi-sector recombination was implemented, the time of exposure was 30%-80% R-R interval, 65 < HR < 80 BPM was used to collect two cardiac cycles, HR ≥ 80 HPM was used to collect three cardiac cycles.The 20G cannula was fixed in the superficial vein of the right upper extremity using a dual barrel high-pressure injector.First, inject 50 -70 ml (320 mg I/ml, 2 ml/kg) of the nonionic contrast agent Youweixian intravenously at a rate of 5 ml/s, and then inject 40 ml of normal saline at the same rate.The entire scanning process was triggered by the surestart software.The monitoring area was centrally located within the thoracic aorta within the extent of the scan.The monitoring area was placed in the thoracic aorta at the central level of the scanning field.The trigger threshold is set to 200 HU.

Preprocessing of CCTA images
Due to the fact that the images come from two centers, the acquisition protocol, scanner, and spatial resolution inevitably affected the obtained radiomics features.Therefore, we first performed a unified pre-processing on all images before extracting features.Image preprocessing can also reduce the risk of overfitting and improve the generalization ability of the models.A software package that performs quantitative analysis (A.K. software, GE Healthcare) was used to preprocess the CCTA images before extracting the radiomics features.Each sequence of the images is resampled to a resolution of 1 x1 x 1 mm 3 through linear interpolation and the gray level of the images needs to be discretized and normalized to 32 orders. of Python (Pyradiomics).Finally, the repeatability of features extracted from inter observers was evaluated using ICCs.To eliminate the central effects of two centers, we used ComBat to normalize and gather the data distributions (Figure S2).

The specific calculation process CT-FFR
CT-fractional flow reserve (CT-FFR) is a new marker that can be obtained from CCTA images using fluid dynamics technology to simulate invasive FFR (1,2).With invasive FFR as the reference standard, CT-FFR has shown high sensitivity in predicting the hemodynamic significance of coronary artery stenosis compared with studies using CCTA alone (3,4).Therefore, we used the PHIGo workstation (Precision health institution, Version 1.5.1) to measure the CT-FFR of target lesion.The specific calculation process of CT-FFR is based on two hypotheses: 1) The CCTA imaging could be regarded as the static arterial first-pass imaging of coronary artery; 2) The venous ICM concentration CV(t) is close to zero.At least 5ml/s of contrast agent shall be injected to optimize the enhancement intensity during the first-pass arterial phase.Subsequently, it enters the early part of the first circulation of non -ionic contrast agent, at which point the contrast agent rarely enter the vein.At this point, we believe that the venous ICM concentration CV(t) is close to zero.We record the concentration as fC a (t) during the arterial input and fCV(t) during the venous output.The calculation formula and example diagram are as follows： The above was the calculation process of CT-FFR.In order to display the calculation of CT-FFR more vividly, the specific process was detailed in Figure 2E.

The formula of Elastic Net Regression and display of optimal signatures.
(

1) The formula of Elastic Net Regression
The ICM attenuates X-rays directly proportionally to iodine content in tissue.where the expected benefit of dimension reduction as same as the expected benefit of refraining from treatment.

FIGURE S1
Automated segmentation of WMH.
Arterial phase images of CCTA in each patient were imported in DICOM format into the CQK analysis platform of PHIgo (version 1.5.1,GE Healthcare) software for automated segmentation of the pericoronary adipose tissue (PCAT) around coronary artery and whole myocardium.Firstly, PCAT around the stenosis lesion on coronary segments ( ≥2 mm) can be accurately delineated according to the 18-segment guidelines on the arterial phase images.Secondly, a simulated three-dimensional myocardial visualization image can be obtained through the segmentation and reconstruction of the main coronary segments of coronary segments for the extraction of whole myocardial tissue.Thirdly, Radiologists A and B evaluated images of all patients for semi-automatic segmentation of PCAT-ROI and myocardial-ROI, and manually corrected images with poor segmentation results.The manual correction of images includes the following important steps: (1) Remove myocardial fifibrous fifilaments; (2) Remove non-cardiomyocardial tissue; (3) Correct segmentation errors of coronary artery segments and three-dimensional myocardium; (4) Correct the ROI segmentation range of PCAT and the whole myocardial tissue in detail.For PCAT-ROI, radiologists A and B independently corrected 121 and 138 patients, respectively.For myocardial ROI, radiologists A and B corrected 51 and 60 patients, respectively.After the above steps were processed, the CT-FFR and pFAI at the target lesion were calculated using semi-automatic software.The detailed process is shown in Figure 2E, F.Then, the radiomics features extraction in pericoronary adipose tissue ROI (PCAT-ROI) and the myocardium ROI (myocardium-ROI) was performed based on an open source package

FFR=
In our study, DCA method was used to evaluate the dimensionality reduction of data by calculate the range of threshold probabilities in which a prediction model was clinically useful.The concept of DCA can be illustrated by the equation below: b represents the influence of unnecessary dimension reduction.If the dimension reduction is guided by the prediction model, d -b is the harm related to a false-positive result compared with a true-negative result.On the contrary, a -c represents the result of rejecting beneficial dimension reduction, in another way, the harm from a false-negative result compared with a true-positive result.Pt represents

Table S1
Clinical and demographic characteristics of all enrolled patients in ZJP Hospital and TCM Hospital Presented as mean ± standard deviation, and Student's t test was performed to compare these variables; b Presented as frequencies and percentages, and Chi-square test was used for the comparisons of these variables.